Ensuring data integrity is basic necessity or back bond in big data processing environment to achieve accurate outcome. Of course, same is applicable while executing any data moving operations with traditional data storage systems (RDBMS, Document Repository etc) through various applications. Data transportation over the network, device to device transfer, ETL process, and many more. In two words, data integrity can be defined as assurance of the accuracy and consistency of data throughout the entire life cycle.
In big data processing environment, data(rest) gets persisted in a distributed manner because of huge volume. So, achieving data integrity on top of it is challenging. Hadoop Distributed File Systems (HDFS) has been efficiently built/developed to store any type of data in a distributed manner in the form of data block (breaks down huge volume of data into a set of individual blocks) with data integrity commitment. There might be multiple reasons to get corrupt data blocks in HDFS, starting from IO operation on the system disk, network failure etc.
Can be reached for real-time POC development and hands-on technical training at [email protected] Besides, to design, develop just as help in any Hadoop/Big Data handling related task. Gautam is a advisor and furthermore an Educator as well. Before that, he filled in as Sr. Technical Architect in different technologies and business space across numerous nations.
He is energetic about sharing information through blogs, preparing workshops on different Big Data related innovations, systems and related technologies.